Dual Challenge of Climate Change and Misinformation: How Misinformation Shapes Vulnerability and Adaptation in Rural Communities in Pakistan
Bibliographic record
Abstract
South Asia is among the world’s most climate-vulnerable regions, with rural farming communities facing increasing exposure to climatic stressors and constrained adaptive capacity. In Pakistan, where agriculture remains the primary livelihood for a large rural population, adaptation to climate change depends not only on economic and institutional resources but also on access to credible climate information. This study examines how misinformation influences climate risk perception, perceived vulnerability, and adaptive decision-making among smallholder farmers in Punjab, Pakistan. The study is grounded in the Model of Proactive Private Adaptation to Climate Change (MPPACC) and extends this framework by conceptualizing misinformation as a cognitive and structural barrier embedded within farmers’ information environments. A mixed-methods research design was employed in Dera Ghazi Khan, a socially marginalized and climate-sensitive district. Quantitative data were collected through a household survey of 202 farming households and analyzed using descriptive statistics and regression analysis. Qualitative data were generated through five focus group discussions and ten key informant interviews with extension agents, community leaders, and local intermediaries and analyzed using thematic analysis. The findings show that misinformation distorts farmers’ perceptions of climate trends and risks, leading to misinterpretation of climatic changes. Exposure to misinformation contributes to heightened perceived vulnerability, fatalism, and declining trust in institutional actors, which together weaken adaptive capacity and reduce the adoption of climate-smart practices. By integrating misinformation into the MPPACC framework, the study advances adaptation theory and highlights the need to strengthen extension services, promote literacy, and treat information systems as a component of resilience policy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".